Investigation of cattle plasma proteome in response to pain and inflammation using next generation proteomics technique, SWATH-MS
Bibliographic record
Abstract
Pain assessment in farm animals has primarily relied on a combination of behavioral and physiological responses, although these are relatively subjective and difficult to quantify. It is essential to develop more effective biomarkers of pain in production animals since they are frequently exposed to routine surgical husbandry procedures. More effective biomarkers of pain would improve welfare, limit the loss of productivity associated with pain and permit better assessment of analgesics. This study aimed to investigate the use of a modern mass spectrometry data independent acquisition strategy, termed Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS), to detect candidate protein biomarkers that are known to associate with nociceptive and inflammatory processes in cattle, which could then be used to assess the efficacy of potential analgesics. Calves were randomly divided into two groups that were either surgically dehorned or subjected to restraint stress, without provision of anaesthesia or analgesia in accordance with current industry standards. Samples were analysed before and after dehorning at multiple timepoints. Significant changes in protein concentrations were detected predominantly at 24 and 96 h following dehorning, including kininogens, proteins associated with the coagulation and complement cascades and serine protease inhibitors. Gene ontology analysis revealed that the identified candidate biomarkers were associated with stress, wound healing, immune response, blood coagulation and the inflammatory and acute phase responses, which could be expected following surgical damage to tissues, but can now be more objectively assessed. These results offer more definitive and quantitative monitoring of response to tissue injury induced pain and inflammation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".